Audience targeting system with segment management

ABSTRACT

Systems, methods and apparatus for delivering content to an audience member over a computer network. A console allows a user to define audience segments that are organized in hierarchical fashion. The segments are then calculated by collecting profile data for audience members and determining whether members have attributes that a defined by the audience segments. The hierarchical definition of segments allows audience segments to be logically combined and facilitates efficient recalculation of audience segments. Profile synchronization provides an authoritative identifier that is used to reconcile the potential issuance of multiple identifiers for a given audience member.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationNo. Ser. 10/669,791, filed on Sep. 25, 2003, and entitled “System andMethod for Segmenting and Targeting Audience Members,” which claims thebenefit under 35 USC § 119 of Provisional Patent Application No.60/491,521, filed on Aug. 1, 2003. The entire contents of theseApplications are hereby incorporated by reference.

This application is also related to U.S. patent application Ser. No.______, filed on Nov. 5, 2004, and entitled “Audience Targeting Systemwith Profile Synchronization” (Attorney Docket No. 54820-00609).

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to audience targeting and moreparticularly to segment management and profile synchronization in anaudience targeting environment.

2. Description of the Related Art

Targeted marketing has long been known as an effective method forreaching consumers. When the consumer receives only relevant content(advertisements, etc.) from a provider, the consumer is more likely topatronize the particular provider, make purchases, and provideadditional personal information that may assist in refining theprovider's “view” of the consumer. As such, targeted marketing can leadto a more focused and robust interaction with the consumer. This,correspondingly, can lead to a more rewarding interaction for theprovider by generating increased revenue.

In order to effectively target a consumer, it may be desirable formarketing systems to react to consumer information received from avariety of online and offline sources. These sources may includedatabases and servers, as well as multiple web properties within anetwork of affiliated websites. Moreover, the consumer information maybe collected from a variety of sources in diverse formats. It may alsobe desirable for marketing systems to interact with the systems thatactually deliver the content to the user. In short, an effectivemarketing system may appreciate the characteristics and preferences of aspecific user regardless of the number or type of channels through whichcontact with the user is made.

Some known systems, however, are only adapted to receive informationfrom a single source (e.g., registration information provided by theconsumer). Other systems may receive information from multiple sources,but are unable to usefully combine information relating to the sameconsumer and communicate it to the necessary content delivery system.Thus, it may be desirable to have a system and method for deliveringcontent that integrates with and aggregates data from various sources,including the underlying systems that deliver content to the consumer.

Known systems for delivering targeted content to consumers are focusedon reaching the greatest quantity of consumers, without considering thevalue of interacting with each particular consumer. For example, somesystems may deliver “targeted” content to each member of a group ofconsumers based on the fact that each subscribes to the same magazine.These systems, however, do not consider that only a portion of the groupmay make on-line purchases, for example, in addition to subscribing tothe magazine. This failure to recognize and differentiate “valuable”consumers can lead to lost revenue for the content provider. Inaddition, the delivery of content to a significant volume of low-valueconsumers may expend valuable system resources. Accordingly, it may bedesirable to have a means of delivering the appropriate content to theappropriate user in order to maximize the value of the relationshipbetween the provider and the consumer.

Another problem with content delivery systems is that the informationupon which targeting is based may rapidly become stale. An audiencemember deemed to have particular characteristics may no longer have suchcharacteristics by the time content is delivered. New potential audiencemembers may also become available after determination of a targetedgroup. The volatility of audience member characteristics and the highvolume of information to be processed both present difficulties tosystems that seek to target well tailored audiences.

Still another problem with content delivery systems, particularly thosethat seek to collect information and deliver content to particularaudience members over the Internet, is the potential for faultyidentification of audience members. For example, some systems may usecookies to attempt to uniquely identify an audience member. Thispresents potential problems because a given person may use severalcomputers and thereby generate several cookies. Software and browsermaintenance activities may also prompt the deletion of cookies.Furthermore, there may be computers that are commonly used by numeroususes. Each of these factors may prompt the proliferation of unnecessaryand sometimes erroneous profiles.

What is needed is an audience targeting system that organizes profiledata in a fashion that is more user friendly and facilitates improvedcalculation and recalculation of audience members to target, as well astechniques for reconciling the proliferation of unnecessary and/orerroneous profiles.

SUMMARY OF THE INVENTION

According to one aspect, the present invention provides an audiencetargeting system and corresponding methods and computer program productsfor managing audience segments. In one embodiment, a hierarchicalarchitecture for defining and managing audience segments is provided.The hierarchical architecture facilitates efficient calculation of themembership of audience segments. Tables that identify lists of audiencemembers belonging to particular segments may be maintained. Thesemembership lists may be logically combined to determine the membershipof dependent (e.g., child) audience segments. The membership in audiencesegments may also be efficiently recalculated by determining audiencemembers who respectively enter and exit audience segments, withcalculation for segments propagating through the hierarchicalarchitecture.

According to another aspect, the present invention provides profilesynchronization. In one embodiment, a profile identifier is a systembased identifier that uniquely identifies an audience member. Anauthoritative identifier (e.g., a registration identifier) is alsosought and maintained in association with a profiled audience member. Anauthoritative identifier may be identified in connection with somecollected profile data. Maintenance of associations betweenauthoritative identifiers and profile identifiers allows such collectedprofile data to be properly associated with a particular audience memberdespite the absence of a profile identifier in the collected data.Maintenance of associations between profile identifiers and external(e.g., cookie) identifiers also allows determination that multiple suchidentifiers correspond to a particular audience member.

The present invention can be embodied in various forms, includingbusiness processes, computer implemented methods, computer programproducts, computer systems and networks, user interfaces, applicationprogramming interfaces, and the like.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other more detailed and specific features of the presentinvention are more fully disclosed in the following specification,reference being had to the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating an example of a system fordelivering content to an audience member.

FIG. 2 is a flow diagram illustrating an example of delivering contentto an audience member.

FIG. 3 is a flow diagram illustrating an example of generating audiencemember profiles.

FIG. 4 is a flow diagram illustrating an example of tracking websitepages visited by an audience member using a unique identifier.

FIG. 5 is a flow diagram illustrating an example of grouping audiencemembers into segments for receipt of targeted content.

FIG. 6 is a flow diagram illustrating an example of directing targetedcontent to audience members in a segment.

FIG. 7 is a block diagram illustrating another example of a system fordelivering content to an audience member.

FIG. 8 is a block diagram illustrating an example of an audiencetargeting system that includes segment management according to oneaspect of the present invention.

FIGS. 9A-B are respectively a block diagram illustrating an example of aparticular extractor 900 and a schematic diagram that exemplifies amodel for extracting profile data according to another aspect of thepresent invention.

FIGS. 10A-B are schematic diagrams illustrating an example of a segmentmanagement architecture and corresponding calculation of segmentsaccording to another aspect of the present invention.

FIGS. 11A-B are schematic diagram illustrating an example of processingdata tables to manage and produce segments according to another aspectof the present invention.

FIG. 12 is a block diagram illustrating an example of an audiencetargeting system that includes profile synchronization according toanother aspect of the present invention.

FIG. 13 is a flow diagram illustrating an example of a process forprofile synchronization.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for purposes of explanation, numerousdetails are set forth, such as flowcharts and system configurations, inorder to provide an understanding of one or more embodiments of thepresent invention. However, it is and will be apparent to one skilled inthe art that these specific details are not required in order topractice the present invention.

One embodiment of the system 10 for delivering content to an audiencemember is shown in FIG. 1. The system 10 includes a first server 120which hosts an extractor program 122. The first server 120 isoperatively connected to one or more offline databases 110, and one ormore external content servers 160. The offline databases 110 andexternal content servers 160 are also operatively connected to one ormore web servers 170. The web servers 170 may provide website pages toan audience member computer 180 in a conventional manner. The webservers 170 are also operatively connected to a targeting engine program152 resident on a second server 150. The first and second servers 120and 150 may be operatively connected to a third server 130 whichcontains a database 132 (referred to as the data warehouse) for storingaudience member profile data. In some embodiments of the presentinvention, the same server may act as the first, second, and/or thirdservers 120, 150, and 130. A control console 140 may be operativelyconnected to the third server 130.

FIG. 2 is a flow diagram illustrating an example of delivering contentto an audience member. This may include three primary stages: datacollection and profile generation; audience segmentation and analysis;and interface to external systems. During data collection and profilegeneration, offline data sources 110 are searched to collect profiledata relating to individuals (referred to as audience members). Thisprofile data is stored in the data warehouse 132. During audiencesegmentation and analysis, the profile data for audience members is usedto categorize the audience members into segments. For example, profiledata may indicate that a particular audience member subscribes to GolfMagazine, and thus has some interest in golf. That audience member maythen be included in a segment (i.e., group) of audience members that arealso interested in golf. During the interface to external systems stage,a targeting engine 152 may use the inclusion of the audience member in asegment to direct targeted external content to the audience members inthat segment. Continuing with the example posed above, audience membersin the “golf” segment may have golf related content sent to them as aresult.

With continued reference to FIG. 2, data collection and profilegeneration may involve the offline databases 110, the extractor program122, and the data warehouse 132. Initial profile information aboutindividual audience members may be collected from available databases,such as a registration database 112 and a subscription database 114 bythe extractor 122. Registration and subscription databases 112 and 114may include audience member profile data collected as a result of theaudience member registering with, or subscribing to, any type ofservice, including but not limited to an Internet, magazine, newspaper,newsletter, cable, telephone, or wireless service, for example. Theseregistration and subscription databases may include a wide variety ofprofile information such as name, gender, age, birth date, home and workaddresses, telephone numbers, credit and charge card information,marital status, income level, number and age of children, employmenthistory, hobbies, recent purchases, educational status, interests,preferences, and the like, for example.

The extractor 122 is a program that is used to parse and identifyaudience member profile data from within a set of data. The extractor122 may be constructed using Java, Perl, C++, C#, SQL, or any othersimilar programming language. The extractor 122 may be resident on aserver 120, or multiple servers. The extractor 122 may be governed by aset of extraction rules that determine the source(s) and format(s) ofprofile data that may be used to create a profile for an audiencemember, and the categories of profile data to be collected. Theextraction rules may include a series of text rules (using matchingutilities such as string matching or regular expressions) that are usedto transform data in one form into a more standardized form whileremoving unneeded data. The extraction rules may include, for example, astatement such as “if string contains ‘A’ then output result ‘B’.”

The extractor 122 is operatively connected to a database 132 referred toas the data warehouse 132. The data warehouse 132 may be provided on asecond server 130, and may be used to store the profile and segmentaffinity data relating to audience members. The extractor 122 mayroutinely update the profile and segment affinity data in the datawarehouse 132. As new or modified profile data becomes available fromthe offline databases 110, the extractor 122 may modify the profile datafor an audience member. The extractor 122 may also receive profile datadirectly from the audience member computer 180 and/or the targetingengine 152 that indicates the website pages visited, the web searchesconducted, and the emails received by the audience member.

FIG. 3 is a flow diagram illustrating an example of generating audiencemember profiles. The steps shown in FIG. 3 show the manner in which theextractor 122 obtains profile data indicating the online website pagesvisited by an audience member. In step 210 the extractor searches theoffline databases, such as registration and subscription databases, forprofile data relating to individual audience members. The search of theoffline databases may be initiated by an instruction received from theconsole 140. For example, an instruction could be given to collectprofile data for all audience members who subscribe to the New YorkTimes. Such an instruction necessitates that the extractor 122 haveaccess to the subscription database for the New York Times.

The extraction rules determine the profile data that is collected. Instep 212, the profile data extracted from the offline sources may bestored in the data warehouse. As there may be a need to determine theprofile data that is associated with a particular audience member, theextractor may assign a unique identifier to the profile data in step214. The unique identifier may be a string of numeric, alphabetic,alphanumeric, or other characters that may be used to identify oneaudience member.

In step 216, the unique identifier may be used to identify contentvisited by the audience member. The unique identifier may be so used byincluding it in a domain cookie associated with each website pagevisited by the audience member. Each of these domain cookies may bestored on the computer associated with the audience member, and may beused to identify each particular website page visited by the audiencemember as being associated with the unique identifier. In step 218, theextractor may determine the domain cookies that are stored on theaudience member's computer. Because these domain cookies include theunique identifier that identifies the particular audience member, theextractor may use these cookies to modify the profile data for aparticular audience member to reflect that the audience member visitedthe website pages associated with the cookies. By combining the profiledata obtained from the offline databases with the profile data updatesthat occur as a result of the audience member visiting website pages, acomplete set of profile data may be collected for an audience member,reflecting both offline and online behavior and characteristics for theaudience member.

Tracking the online history of an audience member requires that thesystem be able to uniquely identify audience members. This tracking maybe accomplished by combining a unique identifier for each audiencemember with website pages in the network that the audience member hasvisited.

A method of providing the unique identifier in each of the domaincookies associated with a number of related website pages is illustratedin FIG. 4. Each of the domain cookies associated with the website pagesvisited by the audience member may be modified to include the uniqueidentifier by designating one of the related website page domains as theprimary website domain. A primary domain cookie with the uniqueidentifier is established for the primary website domain. Usually, anetwork will already have a domain that can be used for this purpose. Ifnot, one of the domains in the network may be designated as the primarydomain.

With reference to FIG. 4, an audience member browser 300 initiates theprocess in step 340 by requesting a website page from a site within thenetwork, www.domain1.com 310. Responsive to the website page requestdirected to www.domain1.com 310, a page is returned to the browser 300with an image tag which may reference the targeting engine 152 atte.domain1.com in step 342. In step 344, an image request is sent fromthe browser 300 to the targeting engine 152. If a unique identifier isnot included in the request, in step 346 a redirect is sent to thebrowser 300 to the targeting engine 152 now referenced aste.primarydomain.com. The redirect includes a reference to the originaltargeting engine reference in step 344, te.domain1.com. For example, theredirect may be http://te.primarydomain.com/blank.gif?te.domain1.com. Instep 348, the browser 300 may send this redirect request tote.primarydomain.com. Responsive to this request, in step 350 aprimarydomain.com cookie containing a unique identifier for the audiencemember is assigned to the browser 300. In step 352, a second redirect ismade of the browser 300 to te.domain1.com, that may include the sameunique identifier as set in the primary domain cookie. For example, theredirect may be http:/te.domain1.com/blank.gif?tid=7dha6wlk9927sha. Instep 354, the redirect request is returned with the originally requestedimage and a domain1.com cookie with the same unique identifier as theprimarydomain.com cookie.

After the process illustrated in FIG. 4 is completed, an audience membervisit to another website in the network, such as www.domain2.com, mayresult in a request for an image at te.domain2.com. If the TargetingEngine 152 does not detect a domain2.com cookie with a unique identifierfollowing the image request, the Targeting Engine 152 may redirect arequest to primarydomain.com for a cookie. Responsive to this request toprimarydomain.com, the primarydomain.com cookie is read and a redirectis sent back to the browser 300 containing the unique identifiercontained in the primary domain.com cookie. The unique identifier in theprimarydomain.com cookie is the same as previously set. The requestedimage may then be sent to the browser 300 along with the domain2.comcookie which may have the same unique identifier as theprimarydomain.com cookie. This process of providing a domain cookie withthe unique identifier is carried out each time the audience membervisits a new website page for the first time so long as the new websiteis related to the other websites in the network from the viewpoint ofthe Targeting Engine.

The Targeting Engine 152 may be a standalone web server, running onApache, and using a MySQL database on a shared server, although theTargeting Engine 152 may be variously realized using alternativesoftware and separate servers for Apache and the database. The TargetingEngine 152 may direct the setting of an additional cookie that maycontain one or more segment identifiers. These cookies may then be usedby other servers, such as, for example, an ad server, an email server, astreaming media server, and/or a web content server, to deliver targetedcontent to a particular audience member based upon one or more segmentsin the cookie.

With renewed reference to FIG. 2, the audience segmentation and analysisstage may be carried out by the data warehouse 132. The data warehouse132 may assign a particular audience member to one or more segmentsbased upon common profile characteristics. A segment of audience membersmay be defined as a group of audience members to which the system user(such as an advertiser) desires to send the same content. For example,returning to the example discussed above, a segment of audience membersmay be defined as all audience members that the system user selects toreceive a particular golf advertisement. The selection of the audiencemembers for receipt of this advertisement may be based on one or moreaudience member characteristics in the profile data.

A method of associating an audience member with a segment is illustratedin FIG. 5. In step 220, the profile data attribute values of audiencemembers who will qualify for inclusion in the segment may be defined bya set of segment rules. The segment rules may be selected using theconsole 140. Any of number and/or range of profile data attribute valuesmay be used to govern qualification for a segment. In step 222, the datawarehouse 132 may search the profile data to determine the audiencemembers that qualify for the audience segment. This search may becarried out at the request of the system user, and if desired, on aroutine basis, such as daily. In this manner, membership in the audiencesegment may be maintained up to date. In step 224, the data warehouse132 may store segment affinity data to indicate the audience membersthat are included in a particular segment. It is appreciated that thesegment affinity data may indicate that an audience member is in morethan one segment. The segment affinity data is defined by a set of rulesbased upon the behavior and characteristics in the audience profile.Once a set of rules that define the segment affinity data areidentified, a segment identifier is assigned to that particular set ofrules. This segment identifier is then sent to the Targeting Engine 152,along with the audience unique identifier assigned previously by theTargeting Engine 152. In step 226, when the Targeting Engine 152 isnotified that an audience member has requested a website page in thenetwork, the Targeting Engine stores a segment-targeting cookie on theaudience member's computer. The segment-targeting cookie includes thesegment identifier that identifies the segments that the audience memberis included in. The method of storing the segment-targeting cookie on anaudience member computer is described in further detail below inconnection with FIG. 6.

Profile data for audience members may also be manually analyzed to buildsegments. With renewed reference to FIG. 2, the server or servers thathost the Targeting Engine 152 and the data warehouse 132 may beoperatively connected to the console 140. The console 140 may be used todesignate the offline databases used to initially populate the datawarehouse with profile information, to set the rules for collectingprofile information, and to create and view reports showing audiencemember profile data, audience member segment affinity data, and audiencemember Internet activity.

A method of delivering targeted content to an audience member based onthe segment affinity data is illustrated in FIG. 6. With reference toFIG. 6, an audience member requests a website page in the network ofrelated websites in step 230. The Targeting Engine is notified of thewebsite page request in step 232. Responsive to the audience membersrequest for a website page, in step 234 the Targeting Engine determineswhether or not a domain cookie, associated with the requested websitepage, includes a unique identifier for the audience member. If a uniqueidentifier is not identified, the Targeting Engine will provide awebsite domain cookie with a unique identifier as described above inconnection with FIG. 4. Once a website domain cookie is provided with aunique identifier, in step 236 the Targeting Engine may determinewhether or not a segment-targeting cookie is already associated with theaudience member in the data warehouse. The segment-targeting cookie mayinclude a segment identifier that indicates the segment(s) to which theaudience member belongs. If segment affinity data is stored in the datawarehouse for the audience member, then a segment-targeting cookie iscreated and stored in the audience member computer with the appropriatesegment identifier in step 238. In step 240, content may be delivered tothe audience member based on the segment identifier in thesegment-targeting cookie stored in the audience member computer.

If no segment-targeting cookie is identified in step 236, the TargetingEngine may query the data warehouse for any segment affinity dataassociated with the audience member. If no segment affinity data isstored for the audience member, a default segment-targeting cookie maybe stored in the audience member computer. The default segment-targetingcookie may automatically expire after some fixed period of time, such asone day for example.

Once a segment-targeting cookie is stored on the audience membercomputer, the Targeting Engine may periodically update it with newsegment affinity data for the audience member. Updating may occurautomatically at fixed intervals, and/or in response to modifications tothe profile data for the audience member.

A wide variety of content may be provided to the audience member as aresult of the segment-targeting cookie being stored on the audiencemember computer. With renewed reference to FIG. 2, content may include,but is not limited to website page advertisements, pop-upadvertisements, emails, or the like.

The system 10 of the present invention is adapted to segment and targetaudience members for delivering content to an audience member across aplurality of digital mediums. The digital mediums may be heterogeneous,and may include, but are not limited to, a website network, a cablesystem, a non-web based internet network, a wireless communicationssystem, such as a cellular phone or RF network, and/or any digitalmedium in which the means for interfacing the audience member with thedigital content is uniquely addressable. It is contemplated that thedigital medium may include other consumer technologies not yetdeveloped.

FIG. 7 is a block diagram illustrating another example of a system fordelivering content to an audience member. The system includes a digitalcable network 400. The digital cable network 400 may include a hometelevision having a uniquely addressable cable set-top box 480 as ameans for interfacing the audience member with digital content. Thedigital cable network 400 may further include a cable head-end 450 fordelivering segment targeted content to the set-top box 480. As will beapparent to those of ordinary skill in the art, the head-end 450 mayinclude means for receiving a digital signal, such as, for example, asatellite receiving antennae, from a programming processor 460. Theprogramming processor 460 programs the content to be delivered to theaudience member, and provides the appropriate digital signal to thehead-end 450. The programming processor 460 may be in communication witha cable company database 430 which may store, for example, subscriptiondata relating to the audience member. The data may include a uniqueidentifier of the audience member within the cable network 400. Theprogramming processor 460 may interface with the system 10 of thepresent invention through a cable network/Internet bridge 440. Asdiscussed above, the system 10 may include an audience member profile.

The digital cable network 400 may further include a cable companywebsite provided by a web server 470 and accessible by the audiencemember via the Internet. The audience member may access the website 470to request a service, such as, for example, ordering a movie, placing arepair order, and changing the level of cable service. The audiencemember may access the website 470 by providing the audience member'scable network identifier.

The system of FIG. 7 may be operated as follows for delivering contentto an audience member across a plurality of digital mediums. Theaudience member may visit a website provided by a web server 170. Theweb server 170 may receive a request for content from the audiencemember, and provide website pages to an audience member computer 180 ina conventional manner. The website 170 may be owned by, or affiliatedwith, the owner of the cable network 400 and the website 470. Theaudience member may visit other sites related to the website 170 withina network. If necessary, a unique audience member identifier related tothe website network is assigned to the audience member, and profile datais collected and stored, substantially as described above in connectionwith FIGS. 3 and 4. The audience member may be associated with anaudience segment defined by a set of segment rules substantially asdescribed above in connection with FIG. 5.

The audience member may visit the website 470 to request a service fromthe cable company, at the same time providing the audience member'sunique identifier within the cable network 400. The programmingprocessor 460 may read the audience member's web network identifier, andassociate the audience member's cable network identifier with thisidentifier. The programming processor 460 may then access the system 10through the bridge 440, and accesses the segment affinity data relatingto the particular audience member using the web network identifier.Based on the audience segment affinity data, the programming processor460 defines the programming rules for the audience segment within thecable network 400. The appropriate digital signal is then sent to thecable head-end 450, and the head-end 450 delivers the audience membertargeted content via the set-top box 480 and the audience member's hometelevision. The preferences and behavior of the audience member withinthe network 400 may also be used to update the member's profile withinthe system 10. In this manner, the audience member's preference andbehavioral data is synchronized across a plurality of mediums into acommon profile, and the content delivered to the audience member viathose mediums may be customized based upon the characteristics of theprofile.

FIG. 8 is a block diagram illustrating an embodiment of an audiencetargeting system 800 that includes a targeting engine (TE) 810,extractor (Extractor) 820, segment manager (SM) 830, and data warehouse850.

The audience targeting system 800 and its components are illustratedcollectively for ease of discussion. As described previously, thevarious components and corresponding functionality may be providedindividually and separately if desired, such as by different serversthat are assigned to the functionality of one or more of the components.

The functionality of the audience targeting system 800 is preferablyprovided by software that may be executed on any conventional processingsystem, such as those previously named or others. In that regard, theaudience targeting system 800 may in turn be a component of a computersystem containing a processor and memory. Although one modular breakdownis shown, it should be understood that the described functionality maybe provided by greater, fewer and/or differently named components.Although a software embodiment is described, the audience targetingsystem 800 may also be provided as hardware or firmware, or anycombination of software, hardware, and/or firmware.

As previously described, audience segments may be variously calculated,such as on a periodic basis. One model for accommodating audiencesegment calculation is a batch processing model. For example, at 24 hourintervals the Audience Targeting System 800 may prompt a recalculationof all necessary audience segments based upon previously extracted dataas well as any newly extracted data that had been discovered since theprevious batch process. While this model is useful for many applicationsand for certain types of extractable data (e.g., data from registrationsources, surveys and 3^(rd) party data), it is not always the best modelto implement. One issue with the batch processing model is that it canbecome computationally expensive, particularly where audience segmentsare recalculated based upon not only previously extracted data, but thenewly extracted data. Another issue is that certain data sources maycontain data that should be acted on more frequently than dictated bythe batch processing interval.

The example of the Audience Targeting System 800 illustrated in FIG. 8accommodates what is referred to as a continuous processing model.Various aspects of the present invention are useful for supportingcontinuous processing (although it should be noted that these aspectsare also useful for any processing model, not just continuousprocessing). According to one aspect, techniques and a correspondinginfrastructure support the regular mining and sending of data to theExtractor 820. This may be referred to as “dock and shuttle” dataextraction support and is described further in connection with FIG. 9below. Another aspect is provision of the segment manager 830 and itscorresponding implementation of the segment management architecture thatis described further in connection with FIGS. 10A-B below. Additionalaspects include recalculation of audience segments based uponincremental data, and processing data tables to manage and produceaudience segments as described further in connection with FIGS. 11A-B.Each of these aspects may be practiced individually or may be variouslycombined in a particular embodiment as desired.

Still referring to FIG. 8, the Audience Targeting System 800accommodates the collection and coordination of data across multiplesites, as well as the targeting of audience members. In that regard, auser that wants to target a particular audience defines audiencesegments of interest. The audience segments correlate to user profiledata that may comprise both characteristic and behavioral data. Thecharacteristic data is often found in registration data and includesattributes such as age, gender, ZIP code, and household income. On theother hand, behaviors may include attributes such as which sections wereviewed on a site (e.g., sports, entertainment, health), whichadvertisements were seen (e.g., mortgage rates, allergy medication),referrers (e.g., AOL, Yahoo), the time of visiting the site (point intime, or range), and the frequency of visits to the site. Audiencesegments may be defined based upon such user profile data. In turn, theaudience segments form the basis for the information that is extractedfor analysis, reporting and targeting audience members in relevantsegments.

Audience targeting in accordance with the present invention is notlimited to web applications. For example profile data might includebehavioral attributes such as programs viewed, time viewed, etc., andcharacteristic attributes such as subscriber IDs or the like inapplications involving a television set top box.

The TE 810 provides the means for assigning and coordinating uniqueidentifiers corresponding to individual audience members. As previouslydescribed, when an audience member logs onto a page for the first time,the TE 810 places a cookie on their browser, which contains a uniqueidentifier. Whenever that audience member returns to the site, theunique identifier is sent back to the TE 810. Based upon the uniqueidentifier, the Audience Targeting System can set a segment cookie,which can be used for the delivery of targeted content such as ads,e-mails, etc. to the audience members computer or other relevant device.The TE 810 may also create logs of this activity. The unique identifiermay be referred to as a profile identifier (PRID).

Another example of an extractor 900 is further described with referenceto FIGS. 9A-B, which respectively are a block diagram illustrating anembodiment of an extractor 900 and a schematic diagram that exemplifiesa model for extracting profile data. Although particular terms such asdock and shuttle are used because they are helpful in conceptuallyillustrating this aspect of the present invention, it is noted thatvarious alternative terminology may be used for elements that performthe same functions.

The Extractor 900 includes a shuttle 902, dock 904 and extraction module906. The functionality of the so-configured Extractor 900 is bestunderstood with concurrent reference to FIG. 9B, which also refers toother elements. The shuttle 902 may be code that resides on the datasource. Its purpose is to mine local data locally and send it to theextractor (more specifically, the dock 904 on the extractor). In oneembodiment, the shuttle 902 accomplishes this by assembling boxes. Thedock 904 receives boxes and, when sufficient boxes are available (or atimeout occurs) creates a pallet 908 out of the boxes. The extractorworks on those pallets 908. In order to accomplish this reliably, it isuseful for the shuttle 902 to know where the source data resides. Forthe previously mentioned batch processing embodiments, it is also usefulfor the shuttle to handle the situation where log files “roll” and arearchived by the customer. In this regard, the shuttle 902 interfaceswith log data such as that provided by conventional log file generatingelements (e.g., Apache).

A data agent may also be employed to assist in the gathering ofinformation from website visitors. This may be provided in the form ofcode that is added to those pages in connection with which datacollection is sought. The code may have header and function callportions that respectively identify the functions and variables that itneeds to operate and ensure that all variables have been collected. Thedata agent may be configured to produce log lines suitable for receiptand processing by the TE. Examples of parameters include the version ofthe data agent, the page referrer, the page URL, time information, andthe PRID. As will be described below in connection with profilesynchronization, a REGID parameter may be provided as well. In additionto association with PRID as described, a cookie may delineate a uniqueREGID for an audience member in the same fashion. Another “cookie list”(CLIST) parameter may be used to identify the list of cookies thatshould be captured.

The dock 904 is the receiving area on the Extractor that manages theordering and processing of pallets. Data from the shuttle 902 may begrouped into what is referred to as boxes. Generally, a box contains asingle event, but in some cases (e.g. OAS logs) a single record maycontain several events. An event may be a time tagged user action on asource server. Examples of events may include a web page view, an adimpression, etc. A pallet 908 may be a collection of boxes, and istypically a collection of data mined from the data source and packagedfor delivery to the extractor dock 906.

Various data sources may be supported by this model, but in oneembodiment web log data is the data source. The shuttle 902 may be apersistent C++ application that processes data from a log file or pipe.Upon startup, the shuttle 902 finds the current log file (or pipe) andopens it for reading. In addition, the shuttle 902 establishes aconnection to the dock 904 in order to be able to deliver pallets 908 tothe extraction module 906 for processing.

The shuttle 902 may be configured to process data in a persistent loopuntil an unrecoverable error or external termination signal occurs.During the processing loop, the shuttle 902 reads up to a configurablenumber of available items (log lines) from the source and packages theminto a box. If there are more items available than the maximum number ofitems, or if the total size of the items are greater than the maximumbox size, the extra lines are written into an overflow buffer and willbe inserted first into the next box created.

Once the box has been created, the shuttle 902 sends the box to the dock904, along with an indication of the size of the box for validationpurposes. The extraction module 906 acknowledges and validates the boxand responds with an acceptance signal before the shuttle 902 will dropthe existing box and repeat the processing loop.

More than one shuttle 902 can connect to a given dock to allow formultiple machines which all serve the same data source (e.g., multipleweb servers responding to a single domain via a load balancer). Datafrom different shuttles 902 in a given dock is sorted into bays. Thesebays contain the unprocessed data for a given data source from a givenshuttle.

The extraction module 908 is preferably configured to handle each datasource type, and may include sub-modules for each different data sourcetype (e.g., one for each of OAS, W3C, IIS, etc.).

Finally, the extraction module 906 is responsible for processing data aspallets from the dock 904 and creating the output that gets sent to thedata warehouse 850 for final import processing. Basically, theextraction module 908 component performs extraction as described inconnection with the previously described embodiment of the Extractor(from FIGS. 1-7). The processed data may be referred to as profile data.In one embodiment, the profile data may be organized and thus providedas fact tables that are described further below.

The segment management aspect is now further described with reference toFIG. 8, which illustrates the segment manager 830 to include a segmentorganization module 832 that includes a console management module 834, asegment generation module 836 that includes a new segment calculationmodule 838 and a segment recalculation module 840, and a reportingmodule 842.

The segment manager 830 accommodates the definition and management ofsegments corresponding to audience members based upon characteristic andbehavioral information. The segments are organized according to ahierarchical logical tree based architecture that allows scalablesegment management and accommodates incremental recalculation ofsegments.

The segment organization module 832 facilitates user-definition ofaudience segments according to this architecture. It operates inconjunction with the console manager 834 which provide interfaces thatallow users to define and configure segments according to the samelogical architecture. These interfaces may be in the form of panels thatillustrate segments and combinations of segments to produce new segmentswhich will be further understood upon explanation of the architecturebelow.

The segment generation module 836 generates segments comprisingappropriate audience members based upon the so-defined audiencesegments. The new segment calculation module 838 calculates newsegments, and the segment recalculation module 840 calculates existingsegments, in particular taking incremental data and recalculating suchsegments, thus avoiding the need to fully calculate the segment asthough it were new each time new data arrives.

The segment generation module 836 may be configured to process segmentscontinuously (e.g., as a Windows service). For each pass, the segmentgeneration module 836 reads a table in the database warehouse 850 thatcatalogs segments, to determine which segments it should process on thatpass. A type identifier associated with the segments may indicatewhether the segments are to be calculated anew, and thus passed to thesegment calculation module 838, or incremental, and thus passed to thesegment recalculation module 840.

Finally, the reporting module 842 communicates with the segmentorganization 832 and segment generation module 836 and producescustomizable reports. The designer is free to structure the reportingoptions as desired. One example of a report is a “Known AudienceInside/Outside” report, which reports on the behavior of an audiencesegment in the sections outside the section behavior that defines thesegment. For example, An Inside/Outside report on viewers of the Newssection would show the audience members behavior inside news and comparethat to all other sections of the site. This may be used to targetvaluable behavior on other parts of the site. Another example of areport is a “Reach and Frequency Report”, which reports on the reach(total audience) and frequency (number of times seen) for one or more adcampaigns. The reporting module 842 may implement conventional reportingtools including but not limited to Crystal Reports as provided byBusiness Objects SA, San Jose Calif.

FIGS. 11A-B are schematic diagrams illustrating an example of a segmentmanagement architecture 1000(a-b) and corresponding calculation ofsegments according to another aspect of the present invention. Asintroduced above, the profile data includes attributes that arecorrelated to audience members, and is the basis of the audience segmentdefinitions that are used to target audience members with advertisementsand/or other content.

Profile data may also be organized as “facts” that have one or moreattributes. For example an “Age” fact may have one attribute—Age.However, an “ID” fact may have several attributes such as the PRID or aregistration identifier (REGID) that uniquely identifies registration atthe site. A “Section” fact may contain attributes for the Section, toplevel Section (that is, if Section is /News/International/Politics, TopLevel Section would be /News), second level section(/News/International), site (site that section belongs to) and full path(Site+Section).

Profile data and the individual attributes comprising the profile datamay be categorized as being (1) Characteristics (e.g., Age, Gender,Household Income); (2) Behaviors (e.g., Page Views, Ad Clicks); (3)PRID; or (4) Business Unit ID, which describes the site that a behavioroccurred on.

The attributes may also be said to have dimensions or values that may bedefined in tables for ease of computation. Moreover, attributes may befurther defined based upon whether they are single or multi-valued. Forexample, Age, Gender, HHI are characteristics for which an audiencemember will only have a single value (e.g., an audience member cannot beboth Male and Female). Conversely, behaviors have multiple values peraudience member and some characteristics (e.g., e-mail newsletterssubscriptions) also have multiple values.

The hierarchical architecture facilitates efficient calculation of themembership of audience segments. Lists of audience members belonging toparticular segments may be maintained. These membership lists may belogically combined to determine the membership of dependent (e.g.,child) audience segments.

As indicated, the segment management architecture 1000 a includes aseries of attribute segments, namely Section 1002, Gender 1004, andHousehold Income (HHI) 1006 as provided in this example. Base segmentshave attributes with particular values that correlate to relevantattribute segments 1002-6. Base segments for any number of attributescould be provided (e.g., different behaviors different sections;different gender, different HHI). The illustrated segments are “VisitedNews” 1010, “Male” 1012, and “HHI>$100K” 1014. Each of these may beconsidered as separate and distinct segments. However, these segmentsmay also be logically combined to create new segments that depend fromthem. For example, the segment “Males who have Visited News” 1020comprises a logical combination of the Males 1012 and Visited News Last1010 segments. Still further, a third level in the hierarchy of segmentsmay be defined as “Males who have Visited News with HHI>$100K” 1030,which comprises a logical combination of the previously describedsegment 1020 with base segment 1014 (HHI>$100K). In this fashion, thesystem may variously organize segments, and this same organization canbe used as the basis for guiding the user through the definition ofsegments via the console manager 834. Notably, there may be instanceswhere a user defines a complex segment directly, wherein the systemautomatically generates the base and any intervening segmentsaccordingly, to facilitate calculation and recalculation of segments.

For ease of illustration, a logical “AND” operation has been described,which basically provides the intersection of two parent segments. Thesegment manager 830 supports various additional logical operations orset expressions, including “EXISTS”, which inserts entries from oneparent; “OR”, which inserts entries from the union of two parents; aswell as “exclusive AND”, and “exclusive OR”. Attribute expressions mayalso be used, such as one which inserts entries from a given parentsegment that match specified criteria.

In addition to providing improved organization of segments, the segmentmanagement architecture 1000 a facilitates proper maintenance of asegment population where incremental profile data is processed, withoutrequiring a full calculation of the segment. That is, introduction ofthe new information to the existing segment is accommodated throughlimited processing involving the new information, in lieu of calculatingthe segment based upon application of its definition to the cumulativeset of data. To accommodate this, entry and exit rules are implemented.An “entry” corresponds to an introduction of audience members to aparticular segment based upon the incremental data, and an “exit”corresponds to a removal of audience members from a segment. Entries arebasically audience members found to currently meet the criteria, butwhom are not yet associated with the previously calculated segment.Exits are the opposite—they are audience members found to no longer meetthe criteria.

FIG. 10B illustrates an entry and exit 1032 functionality for thesegment management architecture 1000 b. As described above, theExtractor continuously populates the data warehouse with profile datathat identifies various attributes. As indicated, a Gender' attributesegment 1004′ is generated responsive to incremental profile data. Thisgenerally represents audience members that have attributes defined underthe attribute segment “Gender” within the incremental profile data.Among those are the previously described “Male” segment 1012. In thatregard, exit and entry membership lists are built. Specifically, allaudience members identified as being male in the incremental profiledata are provided in an entry membership list for the Male segment 1012.Similarly, all those audience members who do not have the relevantattribute (which may be referred to as “not male”) are provided in anexit membership list for the Male segment 1012. Exit and entry rules arethen used to determine how to accommodate an appropriate update to thesegment. The entry may be accommodated by taking the union of theexisting membership in Male 1012 with the membership list in the entrymembership list for Male. The exit may be accommodated by removing fromthe existing membership in Male 1012 those audience members listed inthe exit membership list (actual removal, of course, would only beapplicable for those present prior to the recalculation).

For ease of discussion, focus is made on incremental profile data as itrelates to Gender, but the principle of exit and entry can apply to anysegment including but not limited to Visited News, HHI and others.

Incremental profile data based recalculation also propagates through thehierarchy. This may be variously arranged, again depending upon exit andentry rules, which in turn depends upon the logical relationships of thesegments. For a dependent (child) segment resulting from an ANDoperation such as Males who Visited News 1020, this may compriserepeating application of the above-described entry and exit membershiplists for “Male” to the segment Males who Visited News 1020 in a similarfashion. That is, the entry membership list for Males would be added tothe Males who Visited News 1020 segment, and the exit membership listremoved. Alternatively, base segments Male 1012 and Visited News 1010could be recalculated with their respective entry and exit membershiplists, and then Males who Visited News 1030 could be calculated basedupon the intersection of the updated versions of Male 1012 and VisitedNews 1010.

If desired, recalculation of a dependent segment could also be basedupon a calculation based upon the updated parent segments. Specifically,the entry and exit 1032 functionality could be applied to the basesegments, which could then be used to

FIG. 11A is a schematic diagram illustrating an example of processing1100 data tables to manage and calculate segments according to anotheraspect of the present invention. The illustrated processing correlateswith the segments that are defined in the example of FIGS. 10A-B. Asdescribed, the Extractor operates to collect information about numerousaudience members and provides such information in the data warehouse.That information may be organized so that attributes corresponding toindividual audience members may be identified. The illustrated facttables 1102 a-d are a preferred technique for organizing the informationas such. In one embodiment, each fact in a fact table is associated withan audience member using their unique identifier (PRID). A fact tablecontains all facts related to all users for a particular attribute.Accordingly, there is a section fact table that contains all sectionfacts, an age fact table, a gender fact table, etc. Each row in a tablerepresents a piece of data (characteristic or behavior) associated withonly one audience member (more specifically one PRID).

As described attributes may involve characteristics such as age andgender as well as behaviors such as the number of times that theaudience member has visited a particular section (News, Sports, etc.).At times, an attribute may be determined by looking at multiple piecesof information. Thus, while gender may be a simple determination ofwhether gender=“male”, an attribute that includes frequency informationsuch as how many times an audience member visited a particular sectionmay involve counting the number of entries in a fact table for theaudience member. This counting may also be constrained to those entriesfalling within a particular time period.

Various alternatives may be used to provide the functionality of thefact tables, including different organization of the information. Forexample, the system may alternatively construct a table that provides alisting of attributes for a user identified by a unique PRID. This wouldresult in a number of fact tables respectively corresponding to uniqueaudience members identified by their PRIDs.

As previously described, the Segment Manager accesses the informationstored in the data warehouse and maintains segment definitions, such asthose input by the user seeking certain audience segments. A givensegment is calculated by determining which audience members have theattribute for the given segment. According to this aspect of the presentinvention, the association of audience member identifiers to attributesand hierarchical logical tree based segment architecture accommodatevery efficient calculation (and recalculation) of segments.

A first level of processing 1104 may be used to calculate base segments.This is done by identifying the attribute for a base segment and thendetermining the audience members (or more particularly the listing ofPRIDs) that have that attribute. Presume that segment 1.1 is the“Visited News” segment (see FIG. 10A). In this instance, the SegmentManager examines the fact tables and collect the PRIDs for those facttables that contain this attribute. As indicated in segment table 1106a, this may result in a determination that PRIDs 1, 2, 4, 6, and 7 havethe given attribute. The listing of PRIDs in a segment table may also bereferred to as the “membership list” for the given attribute/segment.Again, there may be millions of members in a segment, the limitedlistings are used for ease of illustration.

The segments may also be identified by identifiers (SEGIDs) in lieu ofthe words and phrases that identify them. Thus associating identifiersSEGID_(x.x) with the noted PRIDs efficiently identifies the audiencemembers with the attribute for computational purposes. Each segment maybe organized in this fashion.

Continuing with the example, segment 1.2 may correlate to the attribute“Male”. Audience member PRID₁ is identified as male, and is listed inthe segment table for segment 1.2, but PRID₂, identified as female, isnot. The table 1106 c for segment 1.3 (HHI>$100K) includes both of thosePRIDs. Again, segment tables for each of the segments may be provided,for x base level segments (1106 a-d).

A next level of segments may then be calculated 1108 from the basesegments. This aspect of the present invention accommodates efficientdetermination of further levels of segments through application ofvarious Boolean operations to the existing segment tables. For example,Segment 2.1 may have been defined as “Visited News” AND “Male”. This isaccommodated by determining the intersection of the PRIDs in those twosegment tables (1106 a, 1106 b). As illustrated, the segment table 1110a for segment 2.1 thus includes PRID₁, PRID₄, and PRID₆ since thoseidentifiers appeared in both of the two base segment tables. Table 1110a thus lists audience member identifiers for the males who have visitedNews. Once again, any number of segments may be calculated 1108 at thislevel, denoted as tables for segments 2.1 through 2.y (1110 a-b).

Still further calculation 1110 accommodates determination of the nextlevel of segments. Segment 3.1 (“Males who have visited News withHHI>$100K”) correlates to a combination of Segment 2.1 (Males who havevisited News) and Segment 1.3 (HHI>$100K). Again, the logical ANDimplements the intersection of the relevant segment tables, whichresults in listing PRID₁ and PRID₄ as belonging to segment 3.1, persegment table 1114 a. Any number of z segments may be calculated 1112(segment tables 1114 a-b).

The segment tables are the membership lists for their respectivesegments, and may be updated accordingly responsive to segmentrecalculation upon receipt of incremental profile data as previouslydescribed. FIG. 11B illustrates how the segment tables are updatedresponsive to recalculation based upon receipt of incremental data.Here, entry and exit is accommodated by tables containing membershiplists, or entry tables and exit tables. As previously describedincremental profile data (denoted respectively as fact tables 1102a′-d′) is received, and entry and exit tables are built based upon suchdata. FIG. 11B illustrates how the information in the entry and exittables is useful for recalculating segments. Suppose that the entrytable for the “Males” Segment 1.2 includes PRID₇ and the exit table forthe same segment includes PRID₄. Application of the exit table wouldprompt PRID₄ to be removed from “Males” Segment 1.2 (as denoted bycross-hatching). Application of the entry table would cause PRID₇ to beadded to the segment (as denoted “entry”). The membership of dependentsegments is also updated according to the previously described logic.That is, because PRID₄ is no longer a member of Males Segment 1.2, it isalso removed from dependent segment Males who have Visited News 2.1.Continuing to the next level of dependency PRID₄ is removed from Segment3.1, but PRID₇ is not added because Segment 3.1 is an AND combination ofSegments 2.1 and 1.3, and PRID₇ is absent from Segment 1.3.

Note that different logical combinations will prompt differentapplication of entry and exit upon recalculation. Segment 2.1 is alogical AND of Segments 1.1 and 1.2; if it were a logical OR combinationof those segments, then PRID₄ would not be removed unless it was alsoremoved from Segment 1.1.

Another aspect of the present invention provides profilesynchronization. People may access various computers throughout the dayand week, such as a home computer, office computer, mall kiosk, or thelike.

As described above, PRIDs are unique identifiers that are used toidentify and gather data regarding unique audience members. In thatregard, when a new visitor (e.g., a woman using her office computer) toa web site is encountered, they are associated with the next availablePRID (e.g., PRID_(A)). Cookies implemented in conjunction with thevisitor's browser then include the particular PRID_(A) and are used tocollect profile data for that visitor. Later on, the same person may useher home computer to visit the web site. Presuming that the homecomputer has not been used to access the site, there will not berecognition that she is the same person, and a new unique PRID(PRID_(B)) will be generated and associated with her behavior andcharacteristics from that computer. There will thus be two separate setsof profile data that actually correspond, unbeknownst to the AudienceTargeting System, to the same person.

Further, the person may use another computer (e.g., mall kiosk) thataccesses the web site, and yet another unique PRID_(C) may be issued.This is problematic in two ways. First, it creates a third separate PRIDfor activity corresponding to the same person. Also, the mall kiosk (oreven home and office computers) may be used by multiple people. Eventhough multiple different people are using the computer and engaging invarious behavior, it will all be tracked as PRID_(C).

Still another problem is potential deletion of cookies. Continuing withthis example, if this audience member deletes cookies on her officecomputer, then correlation with PRID_(A) is lost and she will beperceived as a new visitor on the next web site visit, promptingissuance of PRID_(D) in association with her office computer. This isproblematic because the segments associated with PRID_(D) will notreflect information previously gathered in connection with PRID_(A).Also, PRID_(A) will essentially become a defunct PRID, but will still bewastefully processed by the system.

FIG. 12 is a block diagram illustrating an example of an audiencetargeting system 1200 that includes profile synchronization 1260according to another aspect of the present invention. Profilesynchronization variously corrects and mitigates problems associatedwith these conditions. In one embodiment, the PRID is a system basedidentifier that uniquely identifies an audience member. An authoritativeidentifier (e.g., a registration identifier) is also sought andmaintained in association with a profiled audience member. Anauthoritative identifier may be identified in connection with somecollected profile data. Maintenance of associations betweenauthoritative identifiers and PRIDs allows such collected profile datato be properly associated with a particular audience member despite theabsence of a PRID in the collected data. This functionality alsoaccommodates the potential generation of multiple cookie basedidentifiers by a particular audience member. In contrast to the systemidentifier (PRID), which may also be referred to as an internalidentifier, these cookie based identifiers are examples of externalidentifiers (XIDs). Maintenance of associations between each profiledaudience member's PRID with one or more XIDs allows management ofmultiple external (e.g., cookie based) identifiers in association with aparticular audience member.

Before turning to a more detailed discussion of profile synchronization,it is noted that in embodiments of audience targeting that do notimplement profile synchronization, the XID may essentially equate withthe PRID for the purpose of audience member profile management. It isalso noted that although cookie based XIDs are described, other externalidentifiers such as those that correlate to usage of a non-web devicemay also be implemented.

The Audience Targeting System 1200 includes a TE 1210, Extractor 1220,Segment Manager 1230 and Data Warehouse 1250. These elements areanalogous to the commonly named elements in the previously describedAudience Targeting System (800, FIG. 8) and need not be repeated withregard to the profile synchronization aspect.

As with the previously described system, the Audience Targeting System1200 and its components are illustrated collectively, but may beprovided individually and separately if desired. The functionality ofthe Profile Synchronization module 1260 is preferably provided bysoftware that may be executed on any conventional processing system. Inthat regard, the audience targeting system 1200 (or any sub-module) mayin turn be a component of a computer system containing a processor andmemory. Although one modular breakdown is shown, it should be understoodthat the described functionality may be provided by greater, fewerand/or differently named components. Although a software embodiment isdescribed, the functionality may also be provided as hardware orfirmware, or any combination of software, hardware, and/or firmware.

The Profile Synchronization module 1260 includes an ID Management module1262, an Authoritative ID Recognition module 1264, and an ID Storagemodule 1266 that in turn stores profile identifiers (PRIDs) 1268, REGIDs1270, and XIDs 1272.

Profile synchronization entails a recognition that audience members, andthe potential multiple identifiers that they may become associated with,may be associated with an authoritative identifier (ID). TheAuthoritative ID is in turn used to manage the multiple identifiers aswell as the profile data associated with the audience member. In oneembodiment, the Authoritative ID is associated to registration (e.g.,login credentials, REGID) for the user web site. For example, the website may be The New York Times web site, which might requireregistration and login for usage of certain elements of the site.

The Profile Synchronization module 1260 implements PRIDs to uniquelyidentify audience members even as they generate multiple XIDs. In thatregard, PRIDs may be regarded as system level, or more particularlyAudience Targeting System 1200 level unique identifiers, and XIDs asaudience member machine level based unique identifiers.

To accommodate the profile synchronization functionality, the ID Storagemodule 1266 stores the various ID information, including PRIDs 1268,REGIDs 1270, and XIDs 1272. The ID Management module 1262 organizes theissuance of and relationships between the various ID information. Itaccommodates this by associating the PRID for a particular user asuniquely identifying them on the system. This information may be storedalong with other characteristics information such as the first date thatthe audience member was recognized by the system. Tables and the likemay also be used to associate the audience member's PRID to the XIDsthat are correlated to that audience member using profilesynchronization, as well as to the REGID to accommodate recognition ofaudience members in conjunction with the Authoritative ID Recognitionmodule 1264, which determines the presence of authoritativeidentification and communicates with the ID management module 1262 toensure proper issuance of corresponding unique IDs.

The functionality of the Profile Synchronization module 1260 is furtherdescribed with reference to the flow diagram of FIG. 13, whichillustrates an example of a process 1300 for profile synchronization.

In support of the profile synchronization functionality, a new uniqueXID is associated 1302 with a first time visitor to the web site. Ifregistration is applicable for the session, then the REGID is associatedas well. These functions are provided during regular browsing of pagesand facilitated by the data agent as described above. Also in thedescribed fashion, the data warehouse is populated with profile datacorresponding to audience members. Unique REGIDs are thus alsoassociated to respective sets of profile data along with the uniqueXIDs.

The profile data may be retrieved 1304 from the data warehouse in thepreviously described fahion. In embodiments using fact tables, thismeans that entries identifying both the XID and the REGID will beprovided in association with the listed attributes. The fact tableincludes at least an XID, denoted particularly as XID_(P) in thisexample. A first determination 1306 is made as to whether a REGID isalso included in the fact table. As described, the REGID is used as theauthoritative ID. In its absence, the system seeks to process the databy attempting to associate the fact table with a PRID. As described, alist of XIDs is maintained in association with each PRID. Thisinformation is examined to see whether the particular XID (denotedXID_(P)) is found. If found, it is mapped to at least one PRID. It maybe possible that an XID is mapped to multiple PRIDs. In that case thesystem may choose a random PRID, the first one found, or use anyalgorithm to select one. It should be noted that fact tables may bevariously organized to provide this functionality. In one example ofthis the different attributes (Section, Age, Gender, Referrer, etc.) mayeach have a different table where a particular value is associated to aparticular profile via the PRID.

With profile synchronization, the PRID uniquely identifies audiencemembers for the purpose of segmenting. Accordingly, when it isdetermined 1308 that a particular PRID is associated with the particularXID_(P), segments are calculated 1310 associating the attributes in thefact table to that particular PRID. If a PRID is not determined 1318 tobe associated with XID_(P), then a new PRID_(Q) is issued 1312. Inconjunction with that, XID_(P) is mapped to PRID_(Q), and segments arecalculated accordingly.

If it is determined 1306 that a REGID is present in the fact table, suchis construed as the authoritative ID. This may be the first instancethat the system sees a particular REGID, in which case a PRID isassigned (denoted PRID_(R)) and mapped to the REGID (1316).

If it is determined 1314 that there is already a PRID associated withthe particular REGID (i.e., not the first instance of seeing REGID),then the particular PRID (the unique PRID number for that audiencemember) is associated to the fact table attributes and correspondingsegments. Additionally, if such is not already the case, XID_(P) isincluded 1318 in the list of XID numbers that the system has associatedto the particular PRID.

If desired, the segment manager may also segregate segments for anaudience member using the XID list. For example, a particular audiencemember may have two XIDs associated to their unique PRID. One XID maycorrespond to his home computer and another XID may correspond to hiswork computer. Although the system will (through connection to theauthoritative ID as described above) conclude that he is the same personand that all of the activities from both computers could be commonlysegmented under the unique PRID, the listing of XIDs in association withthat PRID allows the system to generate separate segments if desired.This may in fact be desirable to certain users of the Audience TargetingSystem since in some instances an audience member may have separate homeand office personas in terms of computer usage and desired ad exposure.

Thus embodiments of the present invention produce and provide segmentmanagement and profile synchronization in an audience targetingenvironment. Although the present invention has been described inconsiderable detail with reference to certain embodiments thereof, theinvention may be variously embodied without departing from the spirit orscope of the invention. Therefore, the following claims should not belimited to the description of the embodiments contained herein in anyway.

1. A computer implemented method for delivering content to an audiencemember over a computer network, the method comprising: accommodating thereceipt of input to define a hierarchical organization of audiencesegments based upon profile data that is attributable to audiencemembers, the hierarchical organization of audience segments including afirst audience segment, a second audience segment, and a third audiencesegment that depends from the first audience segment and the secondaudience segment; calculating membership in the third audience segmentby receiving profile data and determining that a first plurality ofaudience members include a combination of attributes defined by thefirst audience segment and the second audience segment; andaccommodating the delivery of content to the first plurality of audiencemembers through the computer network based upon membership in the thirdaudience segment.
 2. The method of claim 1, further comprising:receiving incremental profile data following the calculation ofmembership in the third audience segment; and recalculating membershipin the third audience segment based upon a determination from theincremental profile data that additional audience members haveattributes defined by the first audience segment.
 3. The method of claim2, wherein recalculating membership in the third audience segmentincludes a determination that at least one of the first plurality ofaudience members no longer has the attributes defined by the firstaudience segment.
 4. The method of claim 1, further comprising:maintaining first and second membership lists respectively identifyingaudience members having attributes defined by the first and secondaudience segments; applying a Boolean operation to the first and secondmembership lists in calculating membership in the third audiencesegment.
 5. The method of claim 4, wherein maintaining the first andsecond membership lists comprises: determining at least one attributerespectively defined by the first and second audience segments; andreceiving the profile data and determining those audience members thathave the at least one attribute corresponding to the first and/or secondaudience segments.
 6. The method of claim 1, wherein the content isadvertising, and the profile data includes characteristic and behavioralcomponents.
 7. The method of claim 6, wherein the computer networkincludes the Internet, and the behavioral component includes informationabout Internet browsing activities.
 8. An apparatus for deliveringcontent to an audience member over a computer network, the apparatuscomprising: means for accommodating the receipt of input to define ahierarchical organization of audience segments based upon profile datathat is attributable to audience members, the hierarchical organizationof audience segments including a first audience segment, a secondaudience segment, and a third audience segment that depends from thefirst audience segment and the second audience segment; means forcalculating membership in the third audience segment by receivingprofile data and determining that a first plurality of audience membersinclude a combination of attributes defined by the first audiencesegment and the second audience segment; and means for accommodating thedelivery of content to the first plurality of audience members throughthe computer network based upon membership in the third audiencesegment.
 9. The apparatus of claim 8, further comprising: means forreceiving incremental profile data following the calculation ofmembership in the third audience segment, and recalculating membershipin the third audience segment based upon a determination from theincremental profile data that additional audience members haveattributes defined by the first audience segment.
 10. The apparatus ofclaim 9, wherein recalculating membership in the third audience segmentincludes a determination that at least one of the first plurality ofaudience members no longer has the attributes defined by the firstaudience segment.
 11. The apparatus of claim 8, wherein the means forcalculating membership maintains first and second membership listsrespectively identifying audience members having attributes defined bythe first and second audience segments, and applies a Boolean operationto the first and second membership lists in calculating membership inthe third audience segment.
 12. The apparatus of claim 11, whereinmaintaining the first and second membership lists comprises determiningat least one attribute respectively defined by the first and secondaudience segments, and receiving the profile data and determining thoseaudience members that have the at least one attribute corresponding tothe first and/or second audience segments.
 13. The apparatus of claim 8,wherein the content is advertising, and the profile data includescharacteristic and behavioral components.
 14. The apparatus of claim 13,wherein the computer network includes the Internet, and the behavioralcomponent includes information about Internet browsing activities.
 15. Asystem for delivering content to an audience member over a computernetwork, the system comprising: a segment organization module, whichaccommodates the receipt of input to define a hierarchical organizationof audience segments based upon profile data that is attributable toaudience members, the hierarchical organization of audience segmentsincluding a first audience segment, a second audience segment, and athird audience segment that depends from the first audience segment andthe second audience segment; a segment generation module, in operativecommunication with the segment organization module, which calculatesmembership in the third audience segment by receiving profile data anddetermining that a first plurality of audience members include acombination of attributes defined by the first audience segment and thesecond audience segment; and a targeting engine, in operativecommunication with the segment organization module and the segmentgeneration module, which accommodates the delivery of content to thefirst plurality of audience members through the computer network basedupon membership in the third audience segment.
 16. The system of claim15, wherein the segment calculation module comprises: a segmentrecalculation module, which receives incremental profile data followingthe calculation of membership in the third audience segment, andrecalculating membership in the third audience segment based upon adetermination from the incremental profile data that additional audiencemembers have attributes defined by the first audience segment.
 17. Thesystem of claim 16, wherein recalculating membership in the thirdaudience segment includes a determination that at least one of the firstplurality of audience members no longer has the attributes defined bythe first audience segment.
 18. The system of claim 15, wherein thesegment generation module maintains first and second membership listsrespectively identifying audience members having attributes defined bythe first and second audience segments, and applies a Boolean operationto the first and second membership lists in calculating membership inthe third audience segment.
 19. The system of claim 18, whereinmaintaining the first and second membership lists comprises determiningat least one attribute respectively defined by the first and secondaudience segments, and receiving the profile data and determining thoseaudience members that have the at least one attribute corresponding tothe first and/or second audience segments.
 20. The system of claim 15,wherein the content is advertising, and the profile data includescharacteristic and behavioral components.
 21. The system of claim 20,wherein the computer network includes the Internet, and the behavioralcomponent includes information about Internet browsing activities.
 22. Acomputer program product for delivering content to an audience memberover a computer network, the computer program product stored on acomputer readable medium and adapted to perform operations comprising:accommodating the receipt of input to define a hierarchical organizationof audience segments based upon profile data that is attributable toaudience members, the hierarchical organization of audience segmentsincluding a first audience segment, a second audience segment, and athird audience segment that depends from the first audience segment andthe second audience segment; calculating membership in the thirdaudience segment by receiving profile data and determining that a firstplurality of audience members include a combination of attributesdefined by the first audience segment and the second audience segment;and accommodating the delivery of content to the first plurality ofaudience members through the computer network based upon membership inthe third audience segment.
 23. The computer program product of claim22, wherein the operations further comprise: receiving incrementalprofile data following the calculation of membership in the thirdaudience segment; and recalculating membership in the third audiencesegment based upon a determination from the incremental profile datathat additional audience members have attributes defined by the firstaudience segment.
 24. The computer program product of claim 23, whereinrecalculating membership in the third audience segment includes adetermination that at least one of the first plurality of audiencemembers no longer has the attributes defined by the first audiencesegment.
 25. The computer program product of claim 22, wherein theoperations further comprise: maintaining first and second membershiplists respectively identifying audience members having attributesdefined by the first and second audience segments; applying a Booleanoperation to the first and second membership lists in calculatingmembership in the third audience segment.
 26. The computer programproduct of claim 25, wherein maintaining the first and second membershiplists comprises: determining at least one attribute respectively definedby the first and second audience segments; and receiving the profiledata and determining those audience members that have the at least oneattribute corresponding to the first and/or second audience segments.27. The computer program product of claim 22, wherein the content isadvertising, and the profile data includes characteristic and behavioralcomponents.
 28. The computer program product of claim 27, wherein thecomputer network includes the Internet, and the behavioral componentincludes information about Internet browsing activities.